pytorch/torch/csrc/autograd/function.cpp

108 lines
3.2 KiB
C++

#include <torch/csrc/autograd/function.h>
#include <c10/util/ThreadLocal.h>
#include <torch/csrc/autograd/engine.h>
#include <torch/csrc/autograd/variable.h>
#include <ATen/ATen.h>
#include <algorithm>
#include <cstdint>
#include <memory>
#include <stdexcept>
#include <string>
#include <utility>
#include <vector>
namespace torch {
namespace autograd {
// The current evaluating node. This is useful to assign the current node as a
// parent of new nodes created during the evaluation of this node in anomaly
// mode.
C10_DEFINE_TLS_static(std::shared_ptr<Node>, tls_current_evaluating_node);
#define current_evaluating_node (tls_current_evaluating_node.get())
NodeGuard::NodeGuard(std::shared_ptr<Node> node) {
last_evaluating_node_ = std::move(current_evaluating_node);
current_evaluating_node = std::move(node);
}
NodeGuard::~NodeGuard() {
// restore the previous evaluating node
current_evaluating_node = std::move(last_evaluating_node_);
}
std::shared_ptr<Node> get_current_node() {
return current_evaluating_node;
}
void Node::assign_parent() {
metadata()->assign_parent(current_evaluating_node);
}
auto Node::name() const -> std::string {
return c10::demangle(typeid(*this).name());
}
AnomalyMetadata* Node::metadata() noexcept {
if (!anomaly_metadata_) {
anomaly_metadata_ = Engine::get_default_engine().make_anomaly_metadata();
}
return anomaly_metadata_.get();
}
static void gatherFunctions(
Node* func,
std::vector<std::shared_ptr<Node>>& stack) {
func->release_variables();
for (auto& edge : func->next_edges()) {
if (edge.function.use_count() == 1) {
stack.emplace_back(std::move(edge.function));
} else {
edge.function.reset();
}
}
}
/*
* Fix for #5534: prevent stack overflow on deletion of deep computation graph
*
* Sometimes one can end up with a very big computation graph of Nodes
* and Edges. Each std::shared_ptr<Node> contains a list of Edge, and
* each Edge contains a std::shared_ptr<Node>. Deleting a
* std::shared_ptr<Node> can trigger the recursive deletion of other
* std::shared_ptr<Node>'s: this can stack overflow if the graph
* is deep enough. Here is an example of such a graph:
*
* shared_ptr<Node> -> Edge -> shared_ptr<Node> -> Edge -> ... ->
* shared_ptr<Node>
*
* The solution here is to detect when we are decrementing away the last
* reference to a Node, and when doing so to buffer up the Node's
* that will be recursively decremented. We can then decrement (and free)
* the original Node without causing a recursive cascade, before
* draining the buffer applying the same behavior. This is, in effect,
* converting recursion to a loop, using a heap buffer in place of the
* recursive call stack.
*/
void deleteNode(Node* function) {
// To avoid stack overflow on large computational graphs,
// we need to track reference decrementing and freeing
// on the heap.
function->release_variables();
std::vector<std::shared_ptr<Node>> stack;
gatherFunctions(function, stack);
delete function;
while (!stack.empty()) {
auto func = std::move(stack.back());
stack.pop_back();
gatherFunctions(func.get(), stack);
// Reference count is decremented on the loop backedge.
}
}
} // namespace autograd
} // namespace torch